Bottom Line:
With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced.We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra.Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

ABSTRACTThe use of MALDI-TOF mass spectrometry as a means of analyzing the proteome has been evaluated extensively in recent years. One of the limitations of this technique that has impeded the development of robust data analysis algorithms is the variability in the location of protein ion signals along the x-axis. We studied technical variations of MALDI-TOF measurements in the context of proteomics profiling. By acquiring a benchmark data set with five replicates, we estimated 76% to 85% of the total variance is due to phase variation. We devised a lobster plot, so named because of the resemblance to a lobster claw, to help detect the phase variation in replicates. We also investigated a peak alignment algorithm to remove the phase variation. This operation is analogous to the normalization step in microarray data analysis. Only after this critical step can features of biological interest be clearly revealed. With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced. We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra. Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

f1-cin-01-32: MALDI-TOF spectrum of a pair of replicates A4 and A5. Shown are spectra before (a) and after (b) alignment. The corresponding lobster plots are shown in (c) and (d).

Mentions:
To illustrate technical variations due to phase shift, we plotted the spectra of a pair of replicates, sample A4 and A5, both of which are from the same biological sample A (Figure 1a). Under ideal situations with no technical variation, these two curves should overlap. The real data demonstrates some inconsistency. We can measure inconsistency among the samples using a “distance” measure of some kind (such as a Kullback-Liebler divergence measure or Hellinger distance); we make direct use of one of those measures below. However, in addition to having quantitative metrics of the inconsistency, it is helpful to have visual representations of the differences. We therefore start by plotting the intensities at each bin using a scatter plot to visually examine the curves.

f1-cin-01-32: MALDI-TOF spectrum of a pair of replicates A4 and A5. Shown are spectra before (a) and after (b) alignment. The corresponding lobster plots are shown in (c) and (d).

Mentions:
To illustrate technical variations due to phase shift, we plotted the spectra of a pair of replicates, sample A4 and A5, both of which are from the same biological sample A (Figure 1a). Under ideal situations with no technical variation, these two curves should overlap. The real data demonstrates some inconsistency. We can measure inconsistency among the samples using a “distance” measure of some kind (such as a Kullback-Liebler divergence measure or Hellinger distance); we make direct use of one of those measures below. However, in addition to having quantitative metrics of the inconsistency, it is helpful to have visual representations of the differences. We therefore start by plotting the intensities at each bin using a scatter plot to visually examine the curves.

Bottom Line:
With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced.We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra.Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

ABSTRACTThe use of MALDI-TOF mass spectrometry as a means of analyzing the proteome has been evaluated extensively in recent years. One of the limitations of this technique that has impeded the development of robust data analysis algorithms is the variability in the location of protein ion signals along the x-axis. We studied technical variations of MALDI-TOF measurements in the context of proteomics profiling. By acquiring a benchmark data set with five replicates, we estimated 76% to 85% of the total variance is due to phase variation. We devised a lobster plot, so named because of the resemblance to a lobster claw, to help detect the phase variation in replicates. We also investigated a peak alignment algorithm to remove the phase variation. This operation is analogous to the normalization step in microarray data analysis. Only after this critical step can features of biological interest be clearly revealed. With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced. We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra. Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.